摘要
将光子计数技术与单像素成像结合的单光子压缩成像方法具有成本低、灵敏度高的特点,但该方法使用传统压缩重建算法时重建时间长。基于深度学习的压缩重建网络不仅实现了快速重建,而且可获得更好的重建质量。最近用于单像素成像的压缩重建网络主要基于光探测器工作在模拟方式,采用无噪声或带有加性高斯白噪声的系统仿真数据进行训练。对此,建立了单光子压缩成像系统噪声模型,提出了一种用于单光子压缩成像的抗噪声重建网络(RN)训练方法,使用含有泊松噪声的测量值仿真数据对神经网络进行训练,并搭建单光子压缩成像系统进行验证。实验结果表明,RN能明显提高各种已有压缩重建网络的图像重建质量。在此基础上,提出了一种用于单光子压缩成像的抗噪重建网络(RPN-net),该网络采用跨越式连接结构与阶段式训练方法,实验结果表明其重建性能优于现有的压缩重建网络。
The single-photon compression imaging method,which combines photon counting technology and singlepixel imaging technology,has the characteristics of low cost and ultra-high sensitivity,however it takes a long time to reconstruct images using the traditional compression reconstruction algorithms.Additionally,the compression reconstruction network based on deep learning not only realizes rapid reconstruction,but yields better reconstruction quality.The recent compression reconstruction network used for single-pixel imaging is primarily based on the optical detector working in an analog mode,using the system simulation data without noise or additive white Gaussian noise for neural network training.In this study,a noise model of the single-photon compression imaging system is established,and an anti-noise reconstruction network(RN)training method for single-photon compression imaging is proposed.Simulation data of the measured values with Poisson noise is used to train the neural network,and a single-photon compression imaging system is built for verification.The results show that the RN can significantly improve the image reconstruction quality of the various existing compression reconstruction networks.On this basis,this study proposes an anti-noise reconstruction network(RPN-net)dedicated to single-photon compression imaging.RPN-net adopts a leaping connection structure and progressive training method,and the results show that the reconstruction performance of the RPN-net is better than that of the existing compression reconstruction networks.
作者
祝志太
鄢秋荣
熊乙宁
杨晟韬
方哲宇
Zhu Zhitai;Yan Qiurong;Xiong Yining;Yang Shengtao;Fang Zheyu(School of Information Engineering,Nanchang University,Nanchang,Jiangxi 330031,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第4期250-261,共12页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61865010,61565012)。
关键词
成像系统
压缩感知
光子计数技术
单光子压缩成像
深度学习
泊松噪声
imaging systems
compressed sensing
photon counting technology
single-photon compression imaging
deep learning
Poisson noise
